말 알아듣고 길 찾는 KAIST 로봇, 국제 챌린지 112개 팀 중 1위
A KAIST Robot That Follows Spoken Directions Takes First of 112 Teams at the ECCV Challenge
Part of the September 18, 2026 daily edition
KAIST는 전기및전자공학부 명현 교수 연구팀이 ECCV 2026과 RSS 2026 국제 챌린지에서 각각 1위와 2위를 차지했다고 9월 16일 밝혔습니다. KAIST와 한국전자통신연구원(ETRI) 공동팀은 9월 9일 스웨덴 말뫼에서 열린 ECCV 2026 ’VLNVerse 챌린지’에서 112개 팀 가운데 두 과제 평균 성공률 90.7%로 1위에 올랐습니다. 연구팀은 로봇이 목적지에 도착했다고 판단하면 장소와 물체의 색·모양, 거리를 살펴 주변을 다시 확인하고, 잘못 찾아왔다고 판단하면 새 경로를 탐색하는 ‘CoRe-VLN’ 기술을 적용했습니다. 성공률은 2위 팀과 같았지만 제출 시각이 앞서 최종 1위로 결정됐습니다. 7월 호주 시드니에서 열린 RSS 2026 ’NaviTrace 챌린지’에서는 여러 AI가 목적지 탐색, 위험 요소 확인, 공간 규칙 판단을 나눠 맡는 ’PRISM-Nav’를 적용해 2위를 기록했으며, 비교적 가벼운 ’Gemini 3 Flash’를 쓰고도 주최 측 비교 모델인 ’Gemini 3.1 Pro Preview’보다 높은 성능을 냈습니다. 명현 교수는 이번 성과가 별도의 추가 학습 없이도 AI가 낯선 환경에서 스스로 판단하고 움직일 수 있음을 보여 준 것이라고 말했습니다.
Translation
KAIST announced on 16 September that a research team led by Professor Hyun Myung of its School of Electrical Engineering had placed first and second, respectively, in the ECCV 2026 and RSS 2026 international challenges. A joint team from KAIST and the Electronics and Telecommunications Research Institute (ETRI) took first place among 112 teams at the ECCV 2026 'VLNVerse Challenge', held in Malmö, Sweden, on 9 September, with an average success rate of 90.7% across the two tasks. The team applied a technique called 'CoRe-VLN': once the robot judges that it has reached its destination, it re-checks its surroundings by examining the colours, shapes and distances of the place and objects, and if it decides it has come to the wrong spot, it searches for a new route. Its success rate was the same as the second-placed team's, but it was declared the final winner because it had submitted earlier. At the RSS 2026 'NaviTrace Challenge', held in Sydney, Australia, in July, the team came second using 'PRISM-Nav', in which several AIs divide up the work of searching for the destination, checking for hazards and judging spatial rules; even using the comparatively lightweight 'Gemini 3 Flash', it outperformed the organisers' comparison model, 'Gemini 3.1 Pro Preview'. Professor Myung said the results show that AI can judge and move on its own in unfamiliar environments without any additional training.
Vocabulary
- 낯설다형용사 (adjective)unfamiliar, strange
- 목적지명사 (noun)a destination
- 경로명사 (noun)a route, a path
- 탐색하다동사 (verb)to search, to explore
- 과제명사 (noun)a task (here, a contest task)
- 성공률명사 (noun)a success rate
- 동률명사 (noun)a tie, an equal score
- 주최 측명사 (noun)the organisers
- 성과명사 (noun)a result, an achievement
Grammar points
-았/었다고 판단하면if it judges that it has ...
-았/었다고 quotes a completed action, and 판단하면 (if it judges) makes that judgement a condition: 도착했다고 판단하면, once it judges that it has arrived. It is the natural way to describe a machine's decision step, because the arrival is the robot's belief, not a fact.
Example: 연구팀은 로봇이 목적지에 도착했다고 판단하면 장소와 물체의 색·모양, 거리를 살펴 주변을 다시 확인하고, 잘못 찾아왔다고 판단하면 새 경로를 탐색하는 'CoRe-VLN' 기술을 적용했습니다.
The team applied 'CoRe-VLN', a technique in which the robot, once it judges it has reached the destination, re-checks its surroundings by examining the colours, shapes and distances of the place and objects, and searches for a new route if it judges it has come to the wrong place.
-았/었지만although ... (past), ... but
-지만 after the past marker concedes a completed fact before the contrasting main clause: 성공률은 같았지만 제출 시각이 앞서 1위로 결정됐다, the success rates were the same, but it was decided first because it submitted earlier. It is the frame for any 'tied on X, decided on Y' sentence.
Example: 성공률은 2위 팀과 같았지만 제출 시각이 앞서 최종 1위로 결정됐습니다.
Its success rate was the same as the second-placed team's, but it was declared the final winner because it had submitted earlier.
N 없이도even without N
없이 means 'without', and adding 도 (even) stresses that the result holds despite the absence: 추가 학습 없이도, even without additional training. The 도 is what turns a plain condition into a claim about capability.
Example: 명현 교수는 이번 성과가 별도의 추가 학습 없이도 AI가 낯선 환경에서 스스로 판단하고 움직일 수 있음을 보여 준 것이라고 말했습니다.
Professor Myung said the results show that AI can judge and move on its own in unfamiliar environments even without any additional training.
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